Introduction

For my data, I decided to look into what were checked out during the years 2022-2023 from the Seattle Public Library. With this data, I am trying to find what books and items were checked out the most. Not only that, I also wanted to see what different categories of things were being checked out at the library and how frequently they were being checked out. I wanted to take a look at trends/books/items from this data set because one, it gives me the most recent data I could take a look at. two, it lets me see what others are checking out more frequently than other books and items from the library, which then could help me look further into why that is the case. Is it an award winning book? Is it written by a famous Author? It lets me answer questions that I am unable to answer without this data set.

Summary Information

Write a summary paragraph of findings that includes the 5 values calculated from your summary information R script

For my summary, I focused on finding the highest amounts and averages. For example, I found out that the most checked out category/type of item was a BOOK. Finding this out, it led me to ask which physical book had the highest amount of checkouts from this data, which then I found was Sea of tranquility / Emily St. John Mandel. This then led me to wonder just how much is the average amount of checkouts per month. While looking more into the data set, I found out that the average checkout number per month was 3.3789608^{5}. Which in other words means that there was around 337,896 physical books being checked out per month. That is crazy! Another thing I wanted to look into while doing summaries was how many kids were checking out Dr.Seuss books. When I was a kid, I would often read and check out Seuss books myself and wanted to see if that was still the case with kids today. With further search, I found that at the Seattle Pacific Library, 744.4166667 of Dr.Seuss books were being checked out each month. I’m glad that the books I grew up with are still helping kids learn and have fun while reading books. Finally, the last thing I wanted to look at was which publisher saw the most success with their works. More people reading and checking out their books should mean that their goal for publishing their work was more succesful. So looking further into this, I found out that HarperCollins Publishers Inc. were the publishers with the most checkouts.

The Dataset

Physical vs Digital Checkout Per Month Chart

For this graph, I took a look at the checkout counts per month comparing physical and digital items. I included this chart into my report because I wanted to show how the digital era is here and that digital items are being checked out a growing amount while physical items are decreasing. The first thing I noticed with this chart was that over the months in 2022, the amount of digital checkouts saw an overall increase by the end of the year while physical copies saw a decrease. Something that also surprised me was that not once during any of the months was there a case where more physical items were checked out more than digital items at the library.

Seuss Book Chart

## `summarise()` has grouped output by 'Title'. You can override using the
## `.groups` argument.
## Warning in geom_point(mapping = aes(x = CheckoutMonth, y = Checkouts, color =
## Title, : Ignoring unknown aesthetics: text

For this chart, I took a look at some of Seuss’s books over the span of the year 2022 and looked at how often they were checked out per month. I looked into this data because Seuss is a well known name and these four different books by him all had different and interesting data. First off, out of the four books, I saw that the book Fox in socks was the most popular while it seemed like Happy birthday to you! was the least popular. I also noticed how the rates of the books being checked out increase and decrease go in a very steep rate. it was either checked out a lot, and than checked out way lower the next month. I grew up on Seuss books and I see that it is still being read by people in the library to this day.

Checkout Count Per Month Chart

## Warning in geom_col(mapping = aes(x = CheckoutMonth, y = Checkouts, fill =
## Checkouts, : Ignoring unknown aesthetics: text
## $x
## [1] "Checkout Month"
## 
## $y
## [1] "Number of Checkouts"
## 
## $fill
## [1] "Checkout Points"
## 
## $title
## [1] "Library Checkout Per Month Data"
## 
## attr(,"class")
## [1] "labels"

For my last chart, I decided to focus on finding the total amount of checkouts for each month. I decided to make a column chart because I believed showing the visualizations next to each month would best show which month had lower or higher checkout counts each month. While analyzing this data, I found that August had the highest checkout count out of any month. While connecting all my charts, I also saw something interesting where all the numbers for the charts are down around February and April which I thought was really interesting. I also noticed that the numbers of checkouts were not going lower than 600,000, which helped show me that libraries are still up and running and are in good business.